%Aigaion2 BibTeX export from Knowledge Engineering Publications %Friday 17 December 2021 11:56:14 PM @INPROCEEDINGS{huellermeier20conformal, author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes and Loza Menc{\'{\i}}a, Eneldo}, editor = {Schmid, Ute and Kl{\"{u}}gl, Franziska and Wolter, Diedrich}, month = sep, title = {Conformal Rule-Based Multi-label Classification}, booktitle = {KI 2020: Advances in Artificial Intelligence}, series = {Lecture Notes in Computer Science}, volume = {12325}, year = {2020}, publisher = {Springer, Cham}, isbn = {978-3-030-58284-5}, url = {https://arxiv.org/abs/2007.08145}, doi = {10.1007/978-3-030-58285-2_25}, abstract = {We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rules have the ability to provide natural (non-)conformity scores, which are required by CP, while CP suggests a way to calibrate the assessment of candidate rules, thereby supporting better predictions and more elaborate decision making. We illustrate the potential usefulness of calibrated conformity scores in a case study on lazy multi-label rule learning.} }